The 400 mm annual precipitation fluctuation zone (75°55′–127°6′E and 26°55′–53°6′N) is located in central and western China, which is a transition area from traditional agricultural to animal husbandry. It is extremely sensitive to climatic changes. The corresponding changes of the ecosystem, represented by vegetation, under the dual influences of climate change and human activities are important issues in the study of the regional ecological environment. Based on the Savitzky–Golay (S–G) filtering method, the Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Differential Vegetation Index (NDVI) dataset (NDVI3g) was reconstructed in this paper. Sen’s slope estimation, Mann–Kendall (M–K), multiple regression residual analysis, and the Hurst index were used to quantify the impacts of climate change and human activities on vegetation; in addition, the future persistence characteristics of the vegetation changes trend were analyzed. Vegetation changes in the study area had an obvious spatio-temporal heterogeneity. On an annual scale, the vegetation increased considerably, with a growth rate of 0.50%/10a. The multi-year mean value of NDVI and growth rate of cultivated land were the highest, followed by the forest land and grassland. On a seasonal scale, the vegetation cover increased most significantly in autumn, followed by spring and summer. In the southeastern and central parts of the study area, the vegetation cover increased significantly (P < 0.05), while it decreased significantly in the northeastern and southwestern parts. In summer, the NDVI value of all vegetation types (cultivated land, forest land and grassland) reached the maximum. The change rate of NDVI value for cultivated land reached the highest in autumn (1.57%/10a), forest land reached the highest in spring (1.15%/10a), and grassland reached the highest in autumn (0.49%/10a). The NDVI of cultivated land increased in all seasons, while forest land (−0.31%/10a) and grassland (−0.009%/10a) decreased in winter. Partial correlation analysis between vegetation and precipitation, temperature found that the areas with positive correlation accounted for 66.29% and 55.05% of the total area, respectively. Under the influence of climate change alone, 62.79% of the study area showed an increasing tendency, among which 46.79% showed a significant upward trend (P < 0.05). The NDVI decreased in 37.21% of the regions and decreased significantly in 14.88% of the regions (P < 0.05). Under the influence of human activities alone, the vegetation in the study area showed an upward trend in 59.61%, with a significant increase in 41.35% (P < 0.05), a downward trend in 40.39%, and a significant downward trend in 7.95% (P < 0.05). Vegetation growth is highly unstable and prone to drastic changes, depending on the environmental conditions.

Quantitative assessment can scientifically determine the health status of a regional ecosystem, identify regional eco-environmental problems, and assist in promoting regional sustainable development and environmental management. Taking China’s important ecological function region, the Yellow River affected area as an example, this study constructed an extended evaluation index system based on the pressure-state-response framework, and remote sensing and GIS techniques were used to dynamically evaluate the spatial and temporal characteristics of ecosystem health in the study area. Furthermore, influencing factors on ecosystem health in the study area were extensively analyzed using the GeoDetector model. The results show that the ecosystem health level in the study area shows significant spatial heterogeneity from 1995–2015, and showed a fluctuating change process. Areas with large fluctuations in health level were mainly distributed in extreme climate areas, ecologically fragile areas, on plains and in hilly areas. Spatial differences of ecosystem health were well explained by using the biological abundance index, relief degree of land surface, soil type, annual average precipitation, elevation, annual average temperature, and population density. Influencing factors have significant interactive effects on ecosystem health.

The spread of COVID-19 is having a serious impact on socioeconomic development, and increased environmental risk perception (ERP). ERP provide new ideas for the orderly recovery of society. However, there have been studies that often pay attention to individual factors, and less concerned about the external environment. In fact, ERP will be affected by the external environment and individual factors. We used a Python script to collect 65,277 valid Weibo comments during the COVID-19 epidemic in China to assess urban residents’ environmental risk perception (ERP). SnowNLP emotion analysis was used to measure the ERP of 366 urban in China, and the structural proportion characteristics and spatial-temporal differentiation of ERP were analyzed. Then, an order logistic regression model was used to investigate the relationship between economic level, social security, medical facilities and ERP. The study investigated the Chinese cities have a higher ERP during the COVID-19 period, and it shows marked fluctuations. As COVID-19 spreads, the ERP shows a distribution pattern of “high in the southeast and low in the northwest” with Hu line as the boundary and “from high to low” with Wuhan as the high value center. COVID-19 serves as catalysts for ERP, the impact of COVID-19 is enhanced after socioeconomic factors are considered. The economic level effectively regulates ERP, except the stage of accelerating diffusion. ERP is effectively stabilized by social security and medical facilities. After considering all the variables simultaneously, we found that the mitigation effect of social security and medical facilities on ERP has improved.

Current resident lifestyles pose a significant threat to urban sustainable development. Therefore, low-carbon behavior is receiving increasing attention from scholars and policy makers. Ascertaining residential self-selection is essential in order to study the relationship between the built environment and travel behavior. While several studies have explored the relationship between the urban form, socioeconomic factors, and travel behavior, only a few of them have studied the impact of self-selection on household energy consumption and other forms of consumption, which are also contribute to household carbon emissions. Using large-scale field surveys of 1,485 households and high-resolution images, sourced from Google Maps in 2018, of Zhengzhou city, the present study estimated the low-carbon level of three kinds of behavior: daily energy use at home, daily travel, and daily consumption. The study investigated the influence factors on low-carbon behavior using the hierarchical linear model. We found that residential self-selection impacts both energy use and daily travel. Residents in some built environments consumed less energy at home and contributed less CO₂ emissions through daily travel than others. In particular, individual-level variables significantly affected the low-carbon energy use behavior. The female, elderly, highly educated, married, and working-class residents with children had higher levels of low-carbon energy use. Community-level variables significantly affected the level of low-carbon travel and low-carbon consumption. If residents lived in areas with high density, mixed land use, and high accessibility, their travel mode and consumption behavior would entail low carbon emissions. There is a relationship between individual variables and community variables. Different individual attributes living in the same built environment have different impacts on low-carbon behaviors.